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arXiv 1010.1966astro-ph.CO
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利用宽波段测光改进高红移星系团的恒星形成率和恒星星族年龄的估算

Improving the Estimation of Star formation Rates and Stellar Population Ages of High-redshift Galaxies from Broadband Photometry

Seong-Kook Lee, Henry C. Ferguson, Rachel S. Somerville, Tommy Wiklind, Mauro Giavalisco

摘要:

我们探索了改进高红移恒星形成星系宽带光度测量的恒星形成率和平均恒星星族年龄的方法。我们使用合成光谱模板与各种简单的参数恒星形成的历史,以适应宽带光谱能量分布。这些参数模型被用来推断年龄,恒星形成率和恒星质量的模拟数据集来自星系演化的层次半解析模型。传统的参数模型通常假设恒星形成的速率在初始瞬时上升之后呈指数下降。我们的研究结果表明,恒星形成的历史与恒星形成率的逐渐上升可能是更好的模板,并可能给更好的整体估计的年龄分布和恒星形成率分布的莱曼break星系。对于B-和V-辍学,我们发现最好的简单参数模型是一个恒星形成率随时间线性增加。平均而言,指数下降模型对B-和V-辍学者的年龄预测过高100%和120%,而对于线性增加模型,年龄预测分别过高9%和16%。同样,指数模型分别低估了56%和60%的恒星形成率,而线性增长模型分别低估了15%和22%。对于U型退出者,恒星形成率有峰值(接近z ~ 3)的模型提供了与年龄的最佳匹配--过度预测从110%降低到26%--恒星形成率--预测不足从58%降低到22%。我们分类不同类型的恒星形成历史的半解析模型,并显示如何为不同的类的偏差行为。我们还提供了恒星质量和恒星形成率估计的双波段校准公式。

英文摘要:

We explore methods to improve the estimates of star formation rates and mean stellar population ages from broadband photometry of high redshift star-forming galaxies. We use synthetic spectral templates with a variety of simple parametric star formation histories to fit broadband spectral energy distributions. These parametric models are used to infer ages, star formation rates and stellar masses for a mock data set drawn from a hierarchical semi-analytic model of galaxy evolution. Traditional parametric models generally assume an exponentially declining rate of star-formation after an initial instantaneous rise. Our results show that star formation histories with a much more gradual rise in the star formation rate are likely to be better templates, and are likely to give better overall estimates of the age distribution and star formation rate distribution of Lyman break galaxies. For B- and V-dropouts, we find the best simple parametric model to be one where the star formation rate increases linearly with time. The exponentially-declining model overpredicts the age by 100 % and 120 % for B- and V-dropouts, on average, while for a linearly-increasing model, the age is overpredicted by 9 % and 16 %, respectively. Similarly, the exponential model underpredicts star-formation rates by 56 % and 60 %, while the linearly-increasing model underpredicts by 15 % 22 %, respectively. For U-dropouts, the models where the star-formation rate has a peak (near z ~ 3) provide the best match for age -- overprediction is reduced from 110 % to 26 % -- and star-formation rate -- underprediction is reduced from 58 % to 22 %. We classify different types of star-formation histories in the semi-analytic models and show how the biases behave for the different classes. We also provide two-band calibration formulae for stellar mass and star formation rate estimations.

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